IS WORK OVER?

Automation index

How much of a ML engineer’s job can AI already do?

Training a baseline and a notebook is fast. Shipping it, watching drift, and owning the miss is not.

This percentage does not predict how many people will lose their jobs.

Task comparison

ML engineer

Where the time goes

Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.

The time saving for each task is estimated separately.

TaskShareAIPoints
Train models, features and notebooks.
Estimate

No public source provides a precise figure. This is our estimate.

22%48%+10.6
Evaluate, document and compare runs.
Estimate

No public source provides a precise figure. This is our estimate.

18%42%+7.6
Deploy, monitor and retrain.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Talk to product about what a good error is.
Estimate

No public source provides a precise figure. This is our estimate.

16%15%+2.4
Dirty data, bias and model limits.
Estimate

No public source provides a precise figure. This is our estimate.

14%18%+2.5
On-call, incidents and rollback.
Estimate

No public source provides a precise figure. This is our estimate.

14%16%+2.2
Total100%29%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • Train models, features and notebooks
  • Evaluate, document and compare runs
  • Deploy, monitor and retrain
  • Talk to product about what a good error is
  • Dirty data, bias and model limits
  • On-call, incidents and rollback
  • machine learning engineer
  • ingeniero ml
  • mlops

Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511

Mobile developer

Where the time goes

Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.

The time saving for each task is estimated separately.

TaskShareAIPoints
Screens, API and offline state.
Estimate

No public source provides a precise figure. This is our estimate.

18%52%+9.4
Store review, crashlytics and ANRs.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Real device, the notch and fragmented Android.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
CI, tests and the week's release.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
Product, the redesign and not breaking what people use.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
That it does not crash, and not ignoring the one-star.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%28%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • build apps
  • ship to stores
  • crashlytics
  • offline state
  • ANRs
  • mobile tests
  • fragmented Android
  • store review

Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2514

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